The Vanderbilt Urologic Infection Repository, a Resource for Personalized Clinical Discovery

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SUMMARY - RESOURCE PROJECT Urinary tract infections (UTIs) represent not only one of the most prevalent urologic pathologies, but also one of the most diverse. With both traditional and emerging risk factors, commonly encountered clinical scenarios range from asymptomatic bacteriuria and uncomplicated cystitis to pyelonephritis, bacteremia, and outright urosepsis. The causative microbial agents likewise include a tremendous variety of opportunistic pathogens, with additional (and significant) genotypic/phenotypic diversity among individual strains of these species. On a molecular level, the host-pathogen factors that dictate the balance between urologic health and UTI remain incompletely defined. Generalized models of pathogenesis fail to account for commonly encountered nuances of real-world clinical practice, limiting the ability of physicians to provide care that is both evidence-based and personalized. To combat these challenges, we seek to create the Vanderbilt Urologic Infection Repository (VUIR): a massive (but de-identified) collection of patient-specific clinical information and paired microbial isolates, repurposed from our Medical Center's routine workflow of diagnostic urine cultures. As both an informatic and biologic resource, the VUIR will build on unique foundations that are already in place at Vanderbilt. These include our Synthetic Derivative, an anonymized mirror of our electronic health records, along with microVU, an initiative through which all sterile-site microbial isolates from our Diagnostic Laboratories are systematically retained for academic inquiry. We now propose to expand microVU activities to include Vanderbilt's formidable volume of urine cultures, linking the banked organisms (many thousand annually) to key searchable parameters from the source-patients (e.g. demographics, symptomatology, risk factors), as well as the broader body of data within the Synthetic Derivative. As a two-way bridge, the VUIR will create an opportunity to parse human phenomes in light of microbiologic results, while providing a tremendous quantity of wild-type microbial strains for downstream experimentation, all stratified by human UTI-phenotypes. One particularly exciting application of the VUIR involves genome-wide association studies (GWAS) that network genetic features of pathogens directly to clinical features of their hosts. In addition to building the VUIR, we will mine the repository to select underrepresented microbial targets for whole-genome sequencing. Through a machine-learning approach, the multi-partite genomic features of these strains will be correlated to their hosts' clinical parameters, with an emphasis on Escherichia coli and the global phenotypes of [1] symptomatic UTI and [2] asymptomatic bacteriuria (ASB). The microbial features that distinguish these phenomenological categories remain poorly defined—at least by the simplified metrics considered to date—although a rigorous molecular definition of UTI- vs-ASB would carry significant diagnostic value for physicians and pathogenetic value for investigators. In light of this complexity, we will utilize the VUIR to harmonize bioinformatic and medical informatic data across host and pathogen, as proof-of-concept for this program's broad utility in clinical/basic urology and allied fields.